Detection of people in images or videos has been of interest in recent years in computer vision, image understanding, and image processing communities for various applications. The problem of detecting people is a very challenging one due to the fact that there are practically unlimited variations in configurations and appearances of human figures, particularly in terms of color, posture, and texture.
In the past, researchers have explored people detection mainly using motion information or explicit models (see “Model-Based Vision: A Program to See a Walking Person,” by David Hogg, Image and Vision Computing, Vol. 1, No. 1, February 1983, pp. 5-20).
In recent years, benefiting from the advances in pattern recognition theory, there has been a shift of interest to using object classification approaches for people detection in static images (see “Probabilistic Methods for Finding People,” by S. Ioffe and D. A. Forsyth, International Journal of Computer Vision 43(1), pp. 45-68, 2001). Methods using human body parts configuration structural analysis are also reported recently in the publication “Automatic Detection of Human Nudes,” by D. A. Forsyth and M. M. Fleck in International Journal of Computer Vision 32(1), pp. 63-77, 1999.
However, there is no published work that tackles the problem of automatically outlining human body contours in conjunction with people detection in photographic images.
Outlining a human figure contour is basically an optimal boundary detection problem. Among the global boundary based techniques, graph searching and dynamic programming are popular techniques used to find a globally optimal boundary based on some local cost criteria. Mortensen and Barrett proposed, in 1995, an interactive tool called Intelligent Scissors which they used for image segmentation (see “Intelligent Scissors for Image Composition,” by Eric N. Mortensen, and William A. Barrett, Computer Graphics Proceedings, Annual Conference Series, pp. 191-198, 1995). The Mortensen and Barrett scheme is basically a semi-automatic object contour extraction method that allows a user to enter his knowledge of the image and leave the algorithm to quickly and accurately extract object boundaries of interest. In comparison with the popular snakes or active contour models (see “Snakes: Active Contour Models,” by Michael Kass et al., in Proceedings of the First International Conference on Computer Vision, pp. 259-268, 1987), the Intelligent Scissors can be regarded as a ‘non-parametric’ boundary finding algorithm.
Snakes were introduced as an energy minimizing splines guided by external and internal forces. Snakes are interactively initialized with an approximate boundary contour; subsequently this single contour is iteratively adjusted in an attempt to minimize an energy functional. Formally, snakes are curves that minimize an energy functional with a term for the internal energy of the curve and a term giving external forces to the curve. The internal energy consists of curve bending energy and curve stretching energy. The external forces are linked to the gradient of the image causing the snake to be drawn to the image's edges. In the Kass et al. paper, snakes are numerical solutions of the Euler equations for a functional minimization problem. In the work done by Flikner et al. they converted the snake problem into a parameter estimation problem (see “Intelligent Interactive Image Outlining Using Spline Snakes,” by Myron Flickner et al., Proc. 28th Asilomar Conf. on Signals, Systems, and Computers, Vol. 1, pp. 731-735, 1994).
The original Intelligent Scissors tool is interactively initialized with just a single seed point and it then generates, at interactive speeds, all possible optimal paths from the seed to every other point in the image. Thus, the user is allowed to interactively select the desired optimal boundary segment. As a result, Intelligent Scissors typically requires less time and effort to segment an object than it takes to manually input an initial approximation to an object boundary.
Snakes are globally optimal over the entire curve whereas the Intelligent Scissors boundaries are piece-wise optimal (i.e., optimal between seed points); thus, creating a desirable balance between global optimality and local control. The Intelligent Scissors outperforms the snakes in outlining object boundaries having arbitrary shapes.
It is useful to design a system that can reliably and automatically outline a human figure contour with good precision. This kind of system can be used, for example, in image composition.
There is a need, therefore, for an improved people detection system that overcomes the problems set forth above. Specifically, there is a need to remove the user interaction of outlining a figure's contour and/or edge boundary.
These and other aspects, features and advantages of the present invention will be more clearly understood and appreciated from a review of the following detailed description of embodiments and appended claims, and by reference to the accompanying drawings.